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1.
Anesth Analg ; 130(5): 1133-1146, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32287121

RESUMO

Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.


Assuntos
Pesquisa Biomédica/normas , Registros Eletrônicos de Saúde/normas , Estudos Multicêntricos como Assunto/normas , Avaliação de Resultados em Cuidados de Saúde/normas , Assistência Perioperatória/normas , Melhoria de Qualidade/normas , Pesquisa Biomédica/tendências , Registros Eletrônicos de Saúde/tendências , Humanos , Avaliação de Resultados em Cuidados de Saúde/tendências , Assistência Perioperatória/tendências , Melhoria de Qualidade/tendências
2.
Arthritis Care Res (Hoboken) ; 72(2): 166-175, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31566905

RESUMO

OBJECTIVE: Despite strong recommendations for routine measurement of rheumatoid arthritis (RA) disease activity and associated treatment changes to attain remission/low disease activity, the measurement tools that clinicians use to evaluate RA patients' disease activity and frequency of treatment change have not been well characterized. Therefore, we evaluated different measurement tools that physicians used to assess RA disease activity and associated RA treatment changes. METHODS: Using data from the Rheumatology Informatics System for Effectiveness (RISE) registry from January 2016 through June 2017, and using the following criteria: age ≥18 years, diagnosis of RA (International Classification of Diseases, Ninth and Tenth Revision, codes), ≥2 RISE visits, and ≥1 RA disease activity measure scored in 2016, we classified eligible patients' drug use at the index visit as monotherapy or combination therapy with conventional synthetic (cs) and biologic disease-modifying antirheumatic drugs (bDMARDs). Outcomes include change in treatment over 12 months. Mixed models identified factors associated with treatment change. RESULTS: Among 50,996 eligible patients, 27,274 had longitudinal data. The most commonly used measures were RAPID3 (78.9%) and the Clinical Disease Activity Index (CDAI) (34.2%). The frequency of treatment change during follow-up was relatively low (35.6-54.6%), even for patients with moderate/high disease activity according to RAPID3 or CDAI scores. Older patients (age ≥75 years; adjusted odds ratio [ORadj ] 0.63 [95% confidence interval (95% CI) 0.50-0.78]) and those already receiving combination therapy with csDMARDs (ORadj 0.45 [95% CI 0.33-0.61]) or combination therapy with bDMARDs (ORadj 0.30 [95% CI 0.24-0.38]) were less likely to change RA treatment even after multivariable adjustment. CONCLUSION: Using the American College of Rheumatology's national RISE registry, one- to two-thirds of RA patients failed to change their treatment, even when experiencing moderate/high disease activity. Multimodal interventions directed at both patients and providers are needed to encourage shared decision-making, goal-directed care, and to overcome barriers to treatment escalation.


Assuntos
Antirreumáticos/administração & dosagem , Artrite Reumatoide/tratamento farmacológico , Progressão da Doença , Informática Médica/tendências , Sistema de Registros , Reumatologia/tendências , Idoso , Artrite Reumatoide/epidemiologia , Estudos de Coortes , Registros Eletrônicos de Saúde/tendências , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Resultado do Tratamento , Estados Unidos/epidemiologia
3.
Arthritis Care Res (Hoboken) ; 72(2): 283-291, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-30740931

RESUMO

OBJECTIVE: Applying treat-to-target strategies in the care of patients with rheumatoid arthritis (RA) is critical for improving outcomes, yet electronic health records (EHRs) have few features to facilitate this goal. We undertook this study to evaluate the effect of 3 health information technology (health-IT) initiatives on the performance of RA disease activity measures and outcomes in an academic rheumatology clinic. METHODS: We implemented the 3 following initiatives designed to facilitate performance of the Clinical Disease Activity Index (CDAI): an EHR flowsheet to input scores, peer performance reports, and an EHR SmartForm including a CDAI calculator. We performed an interrupted time-series trial to assess effects on the proportion of RA visits with a documented CDAI. Mean CDAI scores before and after the last initiative were compared using t-tests. Additionally, we measured physician satisfaction with the initiatives. RESULTS: We included data from 995 patients with 8,040 encounters between 2012 and 2017. Over this period, electronic capture of CDAI scores increased from 0% to 64%. Performance remained stable after peer reporting and the SmartForm were introduced. We observed no meaningful changes in disease activity levels. However, physician satisfaction increased after SmartForm implementation. CONCLUSION: Modifications to the EHR, provider culture, and clinical workflows effectively improved capture of RA disease activity scores and physician satisfaction, but parallel gains in disease activity levels were missing. This study illustrates how a series of health-IT initiatives can evolve to enable sustained changes in practice. However, capture of RA outcomes alone may not be sufficient to improve levels of disease activity without a comprehensive treat-to-target program.


Assuntos
Artrite Reumatoide/diagnóstico , Progressão da Doença , Registros Eletrônicos de Saúde/tendências , Pessoal de Saúde/tendências , Análise de Séries Temporais Interrompida/tendências , Melhoria de Qualidade/tendências , Adulto , Idoso , Artrite Reumatoide/epidemiologia , Registros Eletrônicos de Saúde/normas , Feminino , Pessoal de Saúde/normas , Humanos , Análise de Séries Temporais Interrompida/normas , Masculino , Pessoa de Meia-Idade , Melhoria de Qualidade/normas
7.
Comput Inform Nurs ; 37(12): 655-661, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31634164

RESUMO

Use of standardized terminology has been essential for clear, concise, and accurate documentation of client assessments, care plans, and outcomes. The purpose of this study was to create standardized language goals for a case management system that used the Omaha System. A group of nursing informaticists analyzed, refined, and developed revised goals evaluated using medical vocabulary properties. A set of unique goals aligned with the Omaha System was developed with specifically designed characteristics and functionality that allowed individualization and evaluation of goal attainment. Goal statements and ratings were standardized and written to reflect goals a client could attain. The Omaha System goals served as a template for nurse case managers to use in telephonic support with clients and future development of new goals and allowed the organization the ability to generate quality metrics.


Assuntos
Registros Eletrônicos de Saúde/tendências , Padrões de Referência , Documentação/métodos , Documentação/tendências , Registros Eletrônicos de Saúde/normas , Humanos
8.
Nurs Leadersh (Tor Ont) ; 32(2): 19-30, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31613211

RESUMO

Big data using data science methods (data analytics) has the potential to effectively inform strategies to address complex healthcare challenges. However, this potential can only be realized if healthcare professionals have the requisite depth and breadth of knowledge (i.e., informatics competencies). With the emergence of electronic health records (EHRs - commonly known as clinical information systems [CISs]) in healthcare organizations, data analytics that can "interrogate" CIS big data are now possible. In its digitized form, CIS healthcare data meant to support real-time, evidence-based practice decisions and guide new health policy directions remain more of a conceptual promise than a practice reality. Further, the "data rich information poor" phenomenon existing with today's CISs is often the reality for nurses who document more patient information compared to other healthcare professionals and get negligible results in return. However, data science methods when applied to CIS big data are "uncovering" new evidence currently unavailable through traditional data analytic approaches. Big data science is predicted to provide immense opportunities for nurse leaders by offering robust, electronic tools, which support informed decision-making at corporate tables and "arm" all point-of-care/service clinicians with real-time evidence. In this article, we provide a perspective on how the field of data science can enable informatics-savvy nurse executives to lead clinical transformation in the development of the next generation of evidence-based practice, "practice-based evidence."


Assuntos
Big Data , Ciência de Dados/métodos , Enfermeiras Administradoras/tendências , Canadá , Ciência de Dados/tendências , Registros Eletrônicos de Saúde/tendências , Prática Clínica Baseada em Evidências/métodos , Prática Clínica Baseada em Evidências/tendências , Humanos , Competência em Informação , Liderança , Enfermeiras Administradoras/psicologia
9.
Int J Med Inform ; 132: 103971, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31630063

RESUMO

CONTEXT: Adverse events in healthcare are often collated in incident reports which contain unstructured free text. Learning from these events may improve patient safety. Natural language processing (NLP) uses computational techniques to interrogate free text, reducing the human workload associated with its analysis. There is growing interest in applying NLP to patient safety, but the evidence in the field has not been summarised and evaluated to date. OBJECTIVE: To perform a systematic literature review and narrative synthesis to describe and evaluate NLP methods for classification of incident reports and adverse events in healthcare. METHODS: Data sources included Medline, Embase, The Cochrane Library, CINAHL, MIDIRS, ISI Web of Science, SciELO, Google Scholar, PROSPERO, hand searching of key articles, and OpenGrey. Data items were manually abstracted to a standardised extraction form. RESULTS: From 428 articles screened for eligibility, 35 met the inclusion criteria of using NLP to perform a classification task on incident reports, or with the aim of detecting adverse events. The majority of studies used free text from incident reporting systems or electronic health records. Models were typically designed to classify by type of incident, type of medication error, or harm severity. A broad range of NLP techniques are demonstrated to perform these classification tasks with favourable performance outcomes. There are methodological challenges in how these results can be interpreted in a broader context. CONCLUSION: NLP can generate meaningful information from unstructured data in the specific domain of the classification of incident reports and adverse events. Understanding what or why incidents are occurring is important in adverse event analysis. If NLP enables these insights to be drawn from larger datasets it may improve the learning from adverse events in healthcare.


Assuntos
Sistemas de Notificação de Reações Adversas a Medicamentos/normas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/classificação , Registros Eletrônicos de Saúde/tendências , Processamento de Linguagem Natural , Gestão de Riscos/classificação , Gestão de Riscos/normas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/diagnóstico , Registros Eletrônicos de Saúde/normas , Humanos
11.
Comput Inform Nurs ; 37(11): 583-590, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31478922

RESUMO

This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar terms for patient fall history from homecare visit notes (N = 1 149 586) extracted from a large US homecare agency. Several experiments with parameters of word embedding models were conducted to identify the most time-effective and high-quality model. Models with larger word window width sizes (n = 10) that present users with about 50 top potentially similar terms for each (true) term validated by the user were most effective. NimbleMiner can assist in building a thorough vocabulary of fall history terms in about 2 hours. For domains like nursing, this approach could offer a valuable tool for rapid lexicon enrichment and discovery.


Assuntos
Registros Eletrônicos de Saúde/tendências , Processamento de Linguagem Natural , Processo de Enfermagem/tendências , Algoritmos , Humanos , Design de Software
12.
Nurs Adm Q ; 43(4): 329-332, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31479053

RESUMO

Health care costs are growing exponentially. They will continue to erode disposable income, especially among those most in need of health care-the poor and elderly. As the baby boomer generation ages, we will see dramatic growth in health care spending, which will influence the health care market in new ways. Increased government intervention and technological advancements will only further this shift. Factors driving the need for health care transformation include fragmentation, access problems, unsustainable costs, suboptimal outcomes, and disparities of care. Nurses now have more tools (ie, mHealth, telemedicine, and electronic health records) that they can use to provide assistance to their practices outside of acute care settings. These realities are all contributors to an evolving trend: retail health.


Assuntos
Invenções/tendências , Registros Eletrônicos de Saúde/tendências , Custos de Cuidados de Saúde/tendências , Humanos , Telemedicina/métodos , Telemedicina/tendências
14.
Yearb Med Inform ; 28(1): 16-26, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31419814

RESUMO

INTRODUCTION: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. OBJECTIVE: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. METHODS: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. RESULTS: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. CONCLUSION: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.


Assuntos
Inteligência Artificial , Assistência à Saúde , Registros Eletrônicos de Saúde , Inteligência Artificial/tendências , Aprendizado Profundo , Assistência à Saúde/tendências , Registros Eletrônicos de Saúde/tendências , Previsões , Informática Médica/tendências , Pesquisa Farmacêutica , Publicações
15.
PLoS One ; 14(8): e0220369, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31390350

RESUMO

OBJECTIVE: To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. MATERIALS AND METHODS: Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. RESULTS: Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. DISCUSSION: TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities' relocation and increment of citizens (findings 1, 3-4), the impact of strategies (findings 2-3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. CONCLUSIONS: The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.


Assuntos
Viés , Registros Eletrônicos de Saúde/tendências , Hospitais , Humanos , Alta do Paciente , Transferência de Pacientes , Qualidade da Assistência à Saúde , Espanha
16.
IEEE Pulse ; 10(4): 25-27, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31395530

RESUMO

About this Series This is the sixth and last article in a series on the dramatic transformation taking place in health informatics in large part because of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The first article provided background on health care, electronic health record systems for physicians, and the challenges they both face along with the potential of interoperability to help overcome them. The second introduced the basics of the FHIR standard and some suggested resources for those who are interested in its further exploration. The third introduced SMART on FHIR which, based on its wide adoption, has become the default standard FHIR app platform. The fourth looked at clinical decision support, arguably the single most important provider-facing use case for FHIR. The fifth introduced the personal health record and tools that can utilize the data stored in it as an important use case for FHIR in support of patients. This article looks at the future uses of FHIR with a particular emphasis on those that might impact on research uses of health data. The articles in this series are intended to introduce researchers from other fields to this one and assume no prior knowledge of healthcare or health informatics. They are abstracted from the author's recently published book, Health Informatics on FHIR: How HL7's New API is Transforming Healthcare (Springer International Publishing: https://www.springer.com/us/book/9783319934136).


Assuntos
Sistemas de Apoio a Decisões Clínicas/tendências , Assistência à Saúde/tendências , Registros Eletrônicos de Saúde/tendências , Nível Sete de Saúde/tendências , Software/tendências , Humanos
17.
Pediatrics ; 144(2)2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31278210

RESUMO

BACKGROUND AND OBJECTIVES: An early-onset sepsis (EOS) risk calculator tool to guide evaluation and treatment of infants at risk for sepsis has reduced antibiotic use without increased adverse outcomes. We performed an electronic health record (EHR)-driven quality improvement intervention to increase calculator use for infants admitted to a newborn nursery and reduce antibiotic treatment of infants at low risk for sepsis. METHODS: This 2-phase intervention included programming (1) an EHR form containing calculator fields that were external to the infant's admission note, with nonautomatic access to the calculator, education for end-users, and reviewing risk scores in structured bedside rounds and (2) discrete data entry elements into the EHR admission form with a hyperlink to the calculator Web site. We used statistical process control to assess weekly entry of risk scores and antibiotic orders and interrupted time series to assess trend of antibiotic orders. RESULTS: During phase 1 (duration, 14 months), a mean 59% of infants had EOS calculator scores entered. There was wide variability around the mean, with frequent crossing of weekly means beyond the 3σ control lines, indicating special-cause variation. During phase 2 (duration, 2 years), mean frequency of EOS calculator use increased to 85% of infants, and variability around the mean was within the 3σ control lines. The frequency of antibiotic orders decreased from preintervention (7%) to the final 6 months of phase 2 (1%, P < .001). CONCLUSIONS: An EHR-driven quality improvement intervention increased EOS calculator use and reduced antibiotic orders, with no increase in adverse events.


Assuntos
Antibacterianos/uso terapêutico , Registros Eletrônicos de Saúde/tendências , Sepse Neonatal/diagnóstico , Sepse Neonatal/tratamento farmacológico , Diagnóstico Precoce , Registros Eletrônicos de Saúde/normas , Feminino , Humanos , Recém-Nascido , Masculino , Sepse Neonatal/sangue , Medição de Risco/normas , Medição de Risco/tendências
18.
Intern Med J ; 49(7): 923-929, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31295775

RESUMO

The delivery of healthcare, which includes the informed consent process, is moving to a digital environment. This change in informed consent delivery will be associated with opportunities, risks and also unintentional consequences. Physicians are well placed to contribute to the ongoing dialogue about what is needed to make the informed consent process fit for purpose, in the digital age.


Assuntos
Registros Eletrônicos de Saúde/tendências , Consentimento Livre e Esclarecido , Papel do Médico , Relações Médico-Paciente , Humanos
19.
Perm J ; 232019.
Artigo em Inglês | MEDLINE | ID: mdl-31314721

RESUMO

We suggest changes in the electronic health record (EHR) in hospitalized patients to increase EHR usability by optimizing the physician's ability to approach the patient in a problem-oriented fashion and by reducing physician data entry and chart navigation. The framework for these changes is a Physician's Daily Hospital Progress Note organized into 3 sections: Subjective, Objective, and a combined Assessment and Plan section, subdivided by problem titles. The EHR would consolidate information for each problem by: 1) juxtaposing to each problem title relevant medications, key durable results, and limitations; 2) entering in the running lists under Assessment and Plan the most relevant information for that day, including abbreviated versions of relevant reports; and 3) generating a flow sheet in a problem's progress note for any key results tracked daily. To reduce physician EHR navigation, the EHR would place in the Objective section abbreviated versions of notes of other physicians, nurses, and allied health professionals as well as recent orders. The physician would enter only the analysis and plan and new information not included in the EHR. The consolidation of information for each problem would facilitate physician communication at points of transition of care including generation of a problem-oriented discharge summary.


Assuntos
Registros Eletrônicos de Saúde/tendências , Hospitalização , Registros Médicos Orientados a Problemas , Atitude do Pessoal de Saúde , Documentação , Humanos , Modelos Teóricos , Segurança do Paciente
20.
BMC Med ; 17(1): 143, 2019 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-31311603

RESUMO

Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how "big data" can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine-but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.


Assuntos
Inteligência Artificial , Big Data , Bioética , Conhecimentos, Atitudes e Prática em Saúde , Algoritmos , Inteligência Artificial/ética , Inteligência Artificial/provisão & distribução , Inteligência Artificial/tendências , Big Data/provisão & distribução , Bioética/educação , Bioética/tendências , Pesquisa Biomédica/ética , Pesquisa Biomédica/métodos , Pesquisa Biomédica/tendências , Assistência à Saúde/ética , Assistência à Saúde/tendências , Registros Eletrônicos de Saúde/ética , Registros Eletrônicos de Saúde/provisão & distribução , Registros Eletrônicos de Saúde/tendências , Genômica/tendências , Humanos , Disseminação de Informação/métodos , Conhecimento
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